Using if condition inside the TensorFlow graph with tf.cond

From WikiOD

Parameters[edit | edit source]

Parameter Details
pred a TensorFlow tensor of type bool
fn1 a callable function, with no argument
fn2 a callable function, with no argument
name (optional) name for the operation

Remarks[edit | edit source]

  • pred cannot be just True or False, it needs to be a Tensor
  • The function fn1 and fn2 should return the same number of outputs, with the same types.

Basic example[edit | edit source]

x = tf.constant(1.)
bool = tf.constant(True)

res = tf.cond(bool, lambda: tf.add(x, 1.), lambda: tf.add(x, 10.))
# sess.run(res) will give you 2.

When f1 and f2 return multiple tensors[edit | edit source]

The two functions fn1 and fn2 can return multiple tensors, but they have to return the exact same number and types of outputs.

x = tf.constant(1.)
bool = tf.constant(True)

def fn1():
    return tf.add(x, 1.), x

def fn2():
    return tf.add(x, 10.), x

res1, res2 = tf.cond(bool, fn1, fn2)
# tf.cond returns a list of two tensors
# sess.run([res1, res2]) will return [2., 1.]

define and use functions f1 and f2 with parameters[edit | edit source]

You can pass parameters to the functions in tf.cond() using lambda and the code is as bellow.

x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
z = tf.placeholder(tf.float32)

def fn1(a, b):
  return tf.mul(a, b)

def fn2(a, b):
  return tf.add(a, b)

pred = tf.placeholder(tf.bool)
result = tf.cond(pred, lambda: fn1(x, y), lambda: fn2(y, z))

Then you can call it as bellowing:

with tf.Session() as sess:
  print sess.run(result, feed_dict={x: 1, y: 2, z: 3, pred: True})
  # The result is 2.0
  print sess.run(result, feed_dict={x: 1, y: 2, z: 3, pred: False})
  # The result is 5.0

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